Aiming at achieving artificial general intelligence (AGI) for Metaverse, pretrained foundation models (PFMs), e.g., generative pretrained transformers (GPTs), can effectively provide various AI services, such as autonomous driving, digital twins, and AI-generated content (AIGC) for extended reality. With the advantages of low latency and privacy-preserving, serving PFMs of mobile AI services in edge intelligence is a viable solution for caching and executing PFMs on edge servers with limited computing resources and GPU memory. However, PFMs typically consist of billions of parameters that are computation and memory-intensive for edge servers during loading and execution. In this article, we investigate edge PFM serving problems for mobile AIGC services of Metaverse. First, we introduce the fundamentals of PFMs and discuss their characteristic fine-tuning and inference methods in edge intelligence. Then, we propose a novel framework of joint model caching and inference for managing models and allocating resources to satisfy users' requests efficiently. Furthermore, considering the in-context learning ability of PFMs, we propose a new metric to evaluate the freshness and relevance between examples in demonstrations and executing tasks, namely the Age of Context (AoC). Finally, we propose a least context algorithm for managing cached models at edge servers by balancing the tradeoff among latency, energy consumption, and accuracy.

翻译：为实现元宇宙的通用人工智能（AGI），预训练基础模型（如生成式预训练Transformer，即GPT）可有效提供多种AI服务，例如自动驾驶、数字孪生及面向扩展现实的AI生成内容（AIGC）。凭借低延迟和隐私保护的特性，在边缘智能中部署移动AI服务的预训练基础模型，是在计算资源和GPU内存有限的边缘服务器上缓存与执行这些模型的可行方案。然而，预训练基础模型通常包含数十亿参数，在加载和执行过程中对边缘服务器的计算和内存消耗极大。本文研究了面向元宇宙移动AIGC服务的边缘预训练基础模型部署问题。首先，我们介绍了预训练基础模型的基本原理，并讨论了其在边缘智能中的微调与推理方法特性。随后，我们提出了一种联合模型缓存与推理的新型框架，以高效管理模型并分配资源来满足用户请求。此外，针对预训练基础模型的上下文学习能力，我们提出了一种新指标——上下文时效性（Age of Context, AoC），用于评估示例与执行任务间的新鲜度与相关性。最后，我们提出了一种最小上下文算法，通过平衡延迟、能耗与精度之间的权衡，管理边缘服务器上的缓存模型。